The book "New Introduction to Multiple Time Series Analysis" by Professor Dr. Helmut Lütkepohl is a comprehensive guide to the field of multiple time series analysis. The author, who previously wrote "Introduction to Multiple Time Series Analysis" in 1991, has updated and revised the content to reflect significant developments in the field over the past two decades. The new edition includes 49 figures and 36 tables to illustrate the concepts and methods. The book is divided into five parts, covering finite order vector autoregressive (VAR) processes, cointegrated processes, structural and conditional models, infinite order VAR processes, and special topics related to multiple time series. Key topics include vector autoregressive processes, estimation methods, causality analysis, impulse response analysis, forecasting, and model specification and checking. The book also introduces advanced topics such as vector error correction models (VECM), structural VARs, and vector autoregressive moving average (VARMA) models. The preface highlights the changes made from the previous edition, including the addition of new chapters on cointegrated processes, structural models, and special topics. The author emphasizes the importance of matrix algebra and mathematical statistics for understanding the content, and provides a list of prerequisites for students. The book is designed to be an introductory exposition, focusing on explaining underlying ideas rather than achieving maximum generality. The content is supported by numerous exercises, both algebraic and numerical, and examples using matrix-oriented software like GAUSS, MATLAB, or Ox. The book also includes a detailed appendix on matrix and vector operations, multivariate normal distributions, and asymptotic distributions, as well as references and indices for easy reference.The book "New Introduction to Multiple Time Series Analysis" by Professor Dr. Helmut Lütkepohl is a comprehensive guide to the field of multiple time series analysis. The author, who previously wrote "Introduction to Multiple Time Series Analysis" in 1991, has updated and revised the content to reflect significant developments in the field over the past two decades. The new edition includes 49 figures and 36 tables to illustrate the concepts and methods. The book is divided into five parts, covering finite order vector autoregressive (VAR) processes, cointegrated processes, structural and conditional models, infinite order VAR processes, and special topics related to multiple time series. Key topics include vector autoregressive processes, estimation methods, causality analysis, impulse response analysis, forecasting, and model specification and checking. The book also introduces advanced topics such as vector error correction models (VECM), structural VARs, and vector autoregressive moving average (VARMA) models. The preface highlights the changes made from the previous edition, including the addition of new chapters on cointegrated processes, structural models, and special topics. The author emphasizes the importance of matrix algebra and mathematical statistics for understanding the content, and provides a list of prerequisites for students. The book is designed to be an introductory exposition, focusing on explaining underlying ideas rather than achieving maximum generality. The content is supported by numerous exercises, both algebraic and numerical, and examples using matrix-oriented software like GAUSS, MATLAB, or Ox. The book also includes a detailed appendix on matrix and vector operations, multivariate normal distributions, and asymptotic distributions, as well as references and indices for easy reference.